1
|
Ogunpola A, Saeed F, Basurra S, Albarrak AM, Qasem SN. Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases. Diagnostics (Basel) 2024; 14:144. [PMID: 38248021 PMCID: PMC10813849 DOI: 10.3390/diagnostics14020144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/21/2023] [Accepted: 12/25/2023] [Indexed: 01/23/2024] Open
Abstract
Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study's primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study's outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model's diagnostic accuracy for heart disease.
Collapse
Affiliation(s)
- Adedayo Ogunpola
- DAAI Research Group, College of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK; (A.O.); (S.B.)
| | - Faisal Saeed
- DAAI Research Group, College of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK; (A.O.); (S.B.)
| | - Shadi Basurra
- DAAI Research Group, College of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK; (A.O.); (S.B.)
| | - Abdullah M. Albarrak
- Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (A.M.A.); (S.N.Q.)
| | - Sultan Noman Qasem
- Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (A.M.A.); (S.N.Q.)
| |
Collapse
|
2
|
Feng Y, Li B, Fu R, Hao Y, Wang T, Guo H, Ma J, Baier G, Yang H, Feng Q, Zhang L, Liu Y. A simplified coronary model for diagnosis of ischemia-causing coronary stenosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107862. [PMID: 37857024 DOI: 10.1016/j.cmpb.2023.107862] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/26/2023] [Accepted: 10/11/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND AND OBJECTIVE The functional assessment of the severity of coronary stenosis from coronary computed tomography angiography (CCTA)-derived fractional flow reserve (FFR) has recently attracted interest. However, existing algorithms run at high computational cost. Therefore, this study proposes a fast calculation method of FFR for the diagnosis of ischemia-causing coronary stenosis. METHODS We combined CCTA and machine learning to develop a simplified single-vessel coronary model for rapid calculation of FFR. First, a zero-dimensional model of single-vessel coronary was established based on CCTA, and microcirculation resistance was determined through the relationship between coronary pressure and flow. In addition, a coronary stenosis model based on machine learning was introduced to determine stenosis resistance. Computational FFR (cFFR) was then obtained by combining the zero-dimensional model and the stenosis model with inlet boundary conditions for resting (cFFRr) and hyperemic (cFFRh) aortic pressure, respectively. We retrospectively analyzed 75 patients who underwent clinically invasive FFR (iFFR), and verified the model accuracy by comparison of cFFR with iFFR. RESULTS The average computing time of cFFR was less than 2 s. The correlations between cFFRr and cFFRh with iFFR were r = 0.89 (p < 0.001) and r = 0.90 (p < 0.001), respectively. Diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio for cFFRr and cFFRh were 90.7%, 95.0%, 89.1%, 76.0%, 98.0%, 8.7, 0.1 and 92.0%, 95.0%, 90.9%, 79.2%, 98.0%, 10.5, 0.1, respectively. CONCLUSIONS The proposed model enables rapid prediction of cFFR and exhibits high diagnostic performance in selected patient cohorts. The model thus provides an accurate and time-efficient computational tool to detect ischemia-causing stenosis and assist with clinical decision-making.
Collapse
Affiliation(s)
- Yili Feng
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, China
| | - Bao Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, China
| | - Ruisen Fu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, China
| | - Yaodong Hao
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, China
| | - Tongna Wang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, China
| | - Huanmei Guo
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, China
| | - Junling Ma
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, China
| | - Gerold Baier
- Cell and Developmental Biology, University College London, London WC1E 6BT, UK
| | - Haisheng Yang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, China
| | - Quansheng Feng
- Department of Cardiology, the First People's Hospital of Guangshui, Guangshui, Hubei 432700, China
| | - Liyuan Zhang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, China.
| | - Youjun Liu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, China.
| |
Collapse
|
3
|
Zhang X, Mao B, Che Y, Kang J, Luo M, Qiao A, Liu Y, Anzai H, Ohta M, Guo Y, Li G. Physics-informed neural networks (PINNs) for 4D hemodynamics prediction: An investigation of optimal framework based on vascular morphology. Comput Biol Med 2023; 164:107287. [PMID: 37536096 DOI: 10.1016/j.compbiomed.2023.107287] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/06/2023] [Accepted: 07/28/2023] [Indexed: 08/05/2023]
Abstract
Hemodynamic parameters are of great significance in the clinical diagnosis and treatment of cardiovascular diseases. However, noninvasive, real-time and accurate acquisition of hemodynamics remains a challenge for current invasive detection and simulation algorithms. Here, we integrate computational fluid dynamics with our customized analysis framework based on a multi-attribute point cloud dataset and physics-informed neural networks (PINNs)-aided deep learning modules. This combination is implemented by our workflow that generates flow field datasets within two types of patient personalized models - aorta with fine coronary branches and abdominal aorta. Deep learning modules with or without an antecedent hierarchical structure model the flow field development and complete the mapping from spatial and temporal dimensions to 4D hemodynamics. 88,000 cases on 4 randomized partitions in 16 controlled trials reveal the hemodynamic landscape of spatio-temporal anisotropy within two types of personalized models, which demonstrates the effectiveness of PINN in predicting the space-time behavior of flow fields and gives the optimal deep learning framework for different blood vessels in terms of balancing the training cost and accuracy dimensions. The proposed framework shows intentional performance in computational cost, accuracy and visualization compared to currently prevalent methods, and has the potential for generalization to model flow fields and corresponding clinical metrics within vessels at different locations. We expect our framework to push the 4D hemodynamic predictions to the real-time level, and in statistically significant fashion, applicable to morphologically variable vessels.
Collapse
Affiliation(s)
- Xuelan Zhang
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China
| | - Baoyan Mao
- Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yue Che
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China
| | - Jiaheng Kang
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China
| | - Mingyao Luo
- Department of Vascular Surgery, Fuwai Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100037, China; Department of Vascular Surgery, Fuwai Yunnan Cardiovascular Hospital, Affiliated Cardiovascular Hospital of Kunming Medical University, Kunming, 650102, China
| | - Aike Qiao
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Youjun Liu
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Hitomi Anzai
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan
| | - Makoto Ohta
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan
| | - Yuting Guo
- Department of Mechanical Engineering and Science, Kyoto University, Kyoto, 615-8540, Japan
| | - Gaoyang Li
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan.
| |
Collapse
|
4
|
Hall D. Biophysical Reviews: Turning the page from 2022 to 2023. Biophys Rev 2023; 15:1-11. [PMID: 36909962 PMCID: PMC9995637 DOI: 10.1007/s12551-023-01049-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/27/2023] [Indexed: 02/25/2023] Open
Abstract
This Editorial (vol. 15 issue 1-Regular Issue featuring an Issue Focus on the "100th Anniversary of Har Gobind Khorana") first describes the issue contents before providing both, a look back at some journal highlights from 2022, and a look forward to what we can expect from 2023. The Editorial closes with a roundup of new journal access features and an acknowledgement of those supporting the journal.
Collapse
Affiliation(s)
- Damien Hall
- WPI Nano Life Science Institute, Kanazawa University, Kakumamachi, Kanazawa, Ishikawa 920-1164 Japan
- Department of Applied Physics, Aalto University, 00076 Aalto, Finland
| |
Collapse
|